07/22/19
Deliverables:¶
Submit the following two files:
- Your html document that has your Source code and output
- Your ipynb script that has your Source code and output
Objectives:¶
- Use SQL to execute different queries to retrieve data from Chicago Crime dataset and Police Station dataset
- Use Geospatial queries to locate police stations and gun related crimes (with arrest or no arrest) in every district on Choropleth map
- Use Geospatial queries to provide descriptive stat for every district on Choropleth map
- Use Geospatial queries to locate the Block that is the furthest (Maximum Distance) from the police station that has gun related crime resulted in arrest.
Submission Formats :¶
- Complete IPYNB script that has the source code in Python used to access and analyze the data. The code should be submitted as an IPYNB script that can be be loaded and run in Jupyter Notebook for Python.
- Make sure to include your name as part of the IPYNB file name.
- From the File menu select Download As -> HTML (.html) to create an HTML of the IPYNB file.
Formatting Python Code When programming in Python, refer to Kenneth Reitz’ PEP 8: The Style Guide for Python Code: http://pep8.org/ (Links to an external site.)Links to an external site. There is the Google style guide for Python at https://google.github.io/styleguide/pyguide.html (Links to an external site.)Links to an external site. Comment often and in detail.
Descriptions and Requirement Specifications¶
Chicago Crimes¶
In his first state of the uniion address , president Trump mentioned Chicago violance 10 times Trump's State of the Union Address
Chicago has more homicides than New York and Los Angeles combined¶
Columnist Clarence Page wrote an article , published by the Chicago Tribune stated that the city of Chicago had more homicides in the past two years than New York and Los Angeles combined
Chicago Crimes Dataset¶
The CSV file for crimes dataset for the city of Chicago is obtained from the data portal for the city of Chicago. Here is the link for the city of Chicago data portal City of Chicago Data Portal
Loading the Dataset CSV file¶
Three datasets are need for this assignment:
- The Chicago police stations in every district
- The Boundaries.geojson data for district boundaries
- The Crimes dataset
Lets load the CSV file into a DataFrame object and see the nature of the data that we have.
Complete description of the dataset can be found on Chicago city data portal.
Based on Trumps State of the Union Address and the article written by columnist Clarence Page and published by the Chicago Tribune, we are interested to retrieve the data for the past two years and perform different types of spatial queries.
There are few of these queries that we are interested in to help CPD and city of Chicago to plot on a Choropleteh map those districts that have highest gun crimes.
Here are examples of those types of queries:
- Plot on Choropleth map the districts and their Violent Crimes
- Plot on Choropleth map the districts and their Gun related crimes
- Which district is the crime capital of Chicago districts?
- What the crime density per district?
- Plot on Choropleth map those gun related crimes that resulted in arrests
- Plot on Choropleth map the gun related crime that is in the farthest Block from the policy station for every district
Packages you need to Connect PostgreSQL server to load and retrieve Crhicago Crime dataset from the database:
- psycopg2: for PostgreSQL driver
- area: to calculate the area inside of any GeoJSON geometry
- Folium: for Choropleth maps
Since we are using PostGIS in our work, please read and bookmark Chapter 4. Using PostGIS: Data Management and Queries
In [1]:
!pip install psycopg2
Collecting psycopg2 Downloading https://files.pythonhosted.org/packages/5c/1c/6997288da181277a0c29bc39a5f9143ff20b8c99f2a7d059cfb55163e165/psycopg2-2.8.3.tar.gz (377kB) 100% |████████████████████████████████| 378kB 6.6MB/s eta 0:00:01 Building wheels for collected packages: psycopg2 Building wheel for psycopg2 (setup.py) ... done Stored in directory: /Users/EdwardArroyo/Library/Caches/pip/wheels/48/06/67/475967017d99b988421b87bf7ee5fad0dad789dc349561786b Successfully built psycopg2 Installing collected packages: psycopg2 Successfully installed psycopg2-2.8.3
In [2]:
!pip install area
In [3]:
!pip install folium
In [4]:
import folium
from folium import plugins
from folium.plugins import MarkerCluster
import psycopg2
import csv
import pandas as pd
import json
from area import area
from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT
In [5]:
import getpass
pw = getpass.getpass("Password: ")
In [6]:
db_connection = psycopg2.connect(host='129.105.208.229',dbname="chicago_crimes", user="agc4488" , password=pw)
cursor = db_connection.cursor()
In [7]:
db_connection.commit()
Chicago Crimes Dataset¶
The Crimes_2001_to_present.csv is downloaded from Chicago data portal and it has roughly 6.5 million records.
While working on this dataset, It is prudent to make a note of the following:
- Geospatial queries are very demanding for system resources like CPU, Memory, and DISK
- We are interested in the data set of the past 2 years, and when you execute Geospatial type queries, please be advised that these queries slow down your machine.
- Running this script to work on the data of the past 2 years will require roughly 25 minutes to complete. And requires roughly 40 minutes to complete using the dataset of the past 5 years. And requires hours to complete on the entire dataset with at least 16GB memory.
- It is a good idea to take a slice (past two years) of the dataset and store it, that will help improve performance significantly specially for SEARCH and SORT algorithms that are utilized by the database engine.
Algorithm Performance¶
Sort algorithms used by the database engines vary in performance between O($N log N$) and O($ N^{2} $) where $N$ is the size of the number
Search algorithms used by the database engines vary in performance between O($log N$) and O($ N $) where $N$ is the size of the number
Lets start executing different Queries¶
Query #1:¶
- Calculate the total number of crimes in every district and plot that on Choropleth map. In other words, create a Choropleth map where the districts are shaded in proportion to the number of crimes in that district.
In [8]:
cursor.execute("SELECT district, count(district) from crimes GROUP BY district")
rows=cursor.fetchall()
In [9]:
crimes_per_district = pd.DataFrame(rows, columns=['dist_num','number_of_crimes'])
crimes_per_district['dist_num'] = crimes_per_district['dist_num'].astype(str)
crimes_per_district.head()
Out[9]:
In [10]:
crimes_per_district_map = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
In [11]:
## This will yield a warning about choropleth being deprecated in future version...
# crimes_per_district_map.choropleth(geo_data="Boundaries.geojson",
# fill_color='OrRd',
# fill_opacity=0.5,
# line_opacity=1,
# data = crimes_per_district,
# key_on='feature.properties.dist_num',
# columns = ['dist_num', 'number_of_crimes'],
# legend_name="CRIME MAP"
# )
# I pasted a screenshot of this warning in the next (markdown cell)
In [ ]:
## Use this this instead of the code in last cell. Note "choropleth" is now "Choropletth".
folium.Choropleth(geo_data="Boundaries.geojson",
fill_color='OrRd',
fill_opacity=0.5,
line_opacity=1,
data = crimes_per_district,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'number_of_crimes'],
legend_name="CRIMES PER DISTRICT"
).add_to(crimes_per_district_map)
crimes_per_district_map
In [13]:
cursor.execute("""SELECT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
police_stations = cursor.fetchall()
for police_station in police_stations:
police_station_location = (police_station[0],police_station[1])
cursor.execute('''
SELECT district, count(district)
FROM crimes
WHERE district= %s
GROUP BY district
''',[police_station[2]])
districts_crime_numbers = cursor.fetchall()
#print(districts_crime_numbers)
for district in districts_crime_numbers:
folium.Marker(location =
police_station_location,
popup=folium.Popup(
html="District No : %s has Total Number of Crimes:%s" %district ,max_width=450)).add_to(crimes_per_district_map)
- Lets plot the Choropleth map and notice the intensity of color on the different districts
- The Blue POPUP represents the location of police station in the different districts in the map
In [14]:
# db_connection.commit()
In [ ]:
crimes_per_district_map
Query #2:¶
- Calculate the total number of violent crimes in every district and plot that in a table on Choropleth map. In other words, create a Choropleth map where the districts are shaded in proportion to the number of violent crimes in that district.
N.B: A crime is considered a violent crime if the PRIMARY_TYPE of the crimes is THEFT ,ASSAULT,ROBBER,KIDNAPPING,CRIM SEXUAL ASSAULT,BATTERY, or MURDER.
- Then find the total number of crimes in the district for each of these primary types of violent crime and add a popup marker (located at that district's police headquarter) that displays a DataFrame containing this data.
In [16]:
violent_crime_categories='THEFT','ASSAULT','ROBBERY','KIDNAPPING','CRIM SEXUAL ASSAULT','BATTERY','MURDER'
In [17]:
cursor.execute('''
SELECT district, count(district)
FROM crimes
WHERE PRIMARY_TYPE in %s
GROUP BY district
''',[violent_crime_categories])
rows=cursor.fetchall()
violent_crimes_per_district=pd.DataFrame(rows, columns=['dist_num','number_of_violent_crimes'])
violent_crimes_per_district['dist_num'] = violent_crimes_per_district['dist_num'].astype(str)
violent_crimes_per_district
Out[17]:
In [18]:
# violent_crimes_per_district_map= folium.Map(location =(41.8781, -87.6298),zoom_start=11)
# violent_crimes_per_district_map.choropleth(geo_data="Boundaries.geojson",
# fill_color='YlOrRd',
# fill_opacity=0.5,
# line_opacity=1,
# data = violent_crime_per_district,
# key_on='feature.properties.dist_num',
# columns = ['district_num', 'number_of_violent_crimes'],
# legend_name="VOILENT CRIME MAP"
# )
In [ ]:
violent_crimes_per_district_map= folium.Map(location =(41.8781, -87.6298),zoom_start=11)
folium.Choropleth(geo_data="Boundaries.geojson",
fill_color='OrRd',
fill_opacity=0.5,
line_opacity=1,
data = violent_crimes_per_district,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'number_of_violent_crimes'],
legend_name="VIOLENT CRIMES PER DISTRICT"
).add_to(violent_crimes_per_district_map)
violent_crimes_per_district_map
In addition, for each district find the block(s) that has the highest number of gun crimes in that district. Note that there might be a tie for the highest number of gun crimes. You need to find all such blocks. Add a popup marker (located at that district's police headquarter) that displays a DataFrame containing all such block along with the number of gun crimes for that block (i.e. the highest number of crimes for a district).
In [20]:
cursor.execute("""SELECT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
police_stations = cursor.fetchall()
for police_station in police_stations:
police_station_location =(police_station[0],police_station[1])
cursor.execute('''
SELECT PRIMARY_TYPE, count(PRIMARY_TYPE)
FROM crimes
WHERE district =%s AND PRIMARY_TYPE in %s
GROUP BY PRIMARY_TYPE
''',[police_station[2],violent_crime_categories])
data = cursor.fetchall()
# print(data)
violent_crimes_per_district = pd.DataFrame(data, columns=['Description', 'Number of Violent Crimes'])
header = violent_crimes_per_district.to_html(classes='table table-striped table-hover table-condensed table-responsive')
folium.Marker(location=police_station_location, popup=folium.Popup(html="District Number %s - Violent Crimes %s" %(police_station[2],header))).add_to(violent_crimes_per_district_map)
In [21]:
# db_connection.commit()
In [ ]:
violent_crimes_per_district_map
Query #3:¶
- Calculate the total number of gun related violent crimes in every district and plot that in a table on Choropleth map.
N.B: A crime is considered a gun related violent crime if the word "gun" is contained in the DESCRIPTION and the PRIMARY_TYPE of the crimes is THEFT ,ASSAULT,ROBBER,KIDNAPPING,CRIM SEXUAL ASSAULT,BATTERY, or MURDER.
- Then find the total number of crimes in the district for the different DESCRIPTIONs containing the word "gun" and add a popup marker (located at that district's police headquarter) that displays a DataFrame containing this data.
Lets first create a dataframe of violent gun crimes per district.
In [23]:
gun='%GUN%'
cursor.execute('''
SELECT district, count(district)
FROM crimes
WHERE DESCRIPTION::text LIKE %s and PRIMARY_TYPE in %s
GROUP BY district
''',[gun,violent_crime_categories])
rows = cursor.fetchall()
violent_gun_crimes_per_district = pd.DataFrame(rows, columns=['dist_num','number_of_violent_gun_crimes'])
violent_gun_crimes_per_district['dist_num'] = violent_gun_crimes_per_district['dist_num'].astype(str)
violent_gun_crimes_per_district
Out[23]:
In [24]:
# violent_gun_crimes_per_district_map= folium.Map(location =(41.8781, -87.6298),zoom_start=11)
# violent_gun_crimes_per_district_map.choropleth(geo_data="Boundaries.geojson",
# fill_color='YlOrRd',
# fill_opacity=0.5,
# line_opacity=1,
# data = violent_gun_crimes_per_district,
# key_on='feature.properties.dist_num',
# columns = ['dist_num', 'number_of_violent_gun_crimes'],
# legend_name="VIOLENT GUN CRIMES PER DISTRICT"
# )
In [ ]:
violent_gun_crimes_per_district_map= folium.Map(location =(41.8781, -87.6298),zoom_start=11)
folium.Choropleth(geo_data="Boundaries.geojson",
fill_color='OrRd',
fill_opacity=0.5,
line_opacity=1,
data = violent_gun_crimes_per_district,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'number_of_violent_gun_crimes'],
legend_name="VIOLENT GUN CRIMES PER DISTRICT"
).add_to(violent_gun_crimes_per_district_map)
violent_gun_crimes_per_district_map
Now, lets create a dataframe of the different types of violent gun crimes for every district and then plot it on Choropleth map
In [26]:
cursor.execute("""SELECT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
gun='%GUN%'
police_stations = cursor.fetchall()
for police_station in police_stations:
police_station_location = (police_station[0],police_station[1])
cursor.execute('''
SELECT DESCRIPTION, count(DESCRIPTION)
FROM crimes
WHERE district=%s and DESCRIPTION::text LIKE %s and PRIMARY_TYPE in %s
GROUP BY DESCRIPTION
''',[police_station[2],gun, violent_crime_categories])
data=cursor.fetchall()
df = pd.DataFrame(data, columns=['Description', 'Number of Violent Gun Crimes'])
header = df.to_html(classes='table table-striped table-hover table-condensed table-responsive')
folium.Marker(location=police_station_location,popup=folium.Popup(html="District No: %s GUN_Crime: %s" %(police_station[2],header) )).add_to(violent_gun_crimes_per_district_map)
In [27]:
# db_connection.commit()
In [ ]:
violent_gun_crimes_per_district_map
Query #4:¶
- Calculate the crime density per district
In [29]:
district=[]
tarea=[]
with open('Boundaries.geojson') as f:
data = json.load(f)
a = data['features'] # a is a list of district data (dictionaries)
for i in range(len(a)):# a[i] is the dictionary for ith district in Boundaries.geojson
obj=a[i]['geometry'] # list of coordinates for ith district
n= a[i]['properties'] # district number and district label for ith district
district.append(n['dist_num']) # add district number to the district list
# 1 square meter = 1/10000 hectares. area(obj) is in hectares
tarea.append(area(obj)/10000) # add the the area (in hectares) to area list
af=pd.DataFrame({'dist_num': district,'district_area_in_Hectares':tarea})
af['dist_num'] = af['dist_num'].astype(str)
final_data= pd.merge(af, crimes_per_district, on='dist_num', how='inner')
final_data['crime_density'] = round(final_data['number_of_crimes']/(final_data['district_area_in_Hectares']/100))
final_data
Out[29]:
Query #5:¶
- Create Marker Clusters on Choropleth map for those gun related violent crimes that resulted in arrest (green icon) and those that didn't (red icon).
In [30]:
# gun_crime_arrests_map = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
# gun_crime_arrests_map.choropleth(geo_data="Boundaries.geojson",
# fill_color='YlOrRd',
# fill_opacity=0.5,
# line_opacity=1,
# data = violent_gun_crimes_per_district,
# key_on='feature.properties.dist_num',
# columns = ['dist_num', 'number_of_violent_gun_crimes'],
# legend_name="GUN CRIME"
# )
In [ ]:
gun_crime_arrests_map = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
folium.Choropleth(geo_data="Boundaries.geojson",
fill_color='OrRd',
fill_opacity=0.5,
line_opacity=1,
data = violent_gun_crimes_per_district,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'number_of_violent_gun_crimes'],
legend_name="VIOLENT GUN CRIMES PER DISTRICT"
).add_to(gun_crime_arrests_map)
gun_crime_arrests_map
In [32]:
cursor.execute("""SELECT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
gun='%GUN%'
police_stations = cursor.fetchall()
marker_cluster = MarkerCluster().add_to(gun_crime_arrests_map)
for police_station in police_stations:
police_station_location = (police_station[0],police_station[1])
cursor.execute('''
SELECT DISTINCT ON(caseno)
caseno, block,DESCRIPTION, count(arrest), arrest,latitude, longitude
FROM crimes
WHERE district=%s and DESCRIPTION::text LIKE %s GROUP BY caseno,block,
DESCRIPTION,arrest, latitude, longitude
''',[police_station[2],gun])
crimes_per_district = cursor.fetchall()
for crime in crimes_per_district:
if crime[4]==True:
folium.Marker(location=(crime[5],crime[6]),popup=folium.Popup(html="District No: %s <br> Description: %s <br> Block: %s" %(police_station[2],crime[2],crime[1])),icon=folium.Icon(color='green', icon='ok-sign'),).add_to(marker_cluster)
else:
folium.Marker(location=(crime[5],crime[6]),popup=folium.Popup(html="District No: %s <br> Description: %s<br> Block: %s" %(police_station[2],crime[2],crime[1])),icon=folium.Icon(color='red', icon='remove-sign'),).add_to(marker_cluster)
In [43]:
db_connection.commit()
In [34]:
def embed_map(m):
from IPython.display import IFrame
m.save('index.html')
return IFrame('index.html', width='100%', height='750px')
In [ ]:
embed_map(gun_crime_arrests_map)
Query #6:¶
- Plot on Choropleth map the farthest Block that has a gun crime from every police station in every district
Locate the farthest gun crime from the police station in every district. Create a Choropleth map where the districts are shaded in proportion to the number of gun crimes in that district. For each district, find the gun crime that was farthest from police station. Add a pop-up on the Choropleth map to display the district number and the Block where the farthest gun crime occurred. Also add circle marker (of radius 5) at the location of the farthest** gun crime.
In [36]:
gun='%GUN%'
cursor.execute('''
SELECT district, count(district)
FROM crimes
WHERE DESCRIPTION::text LIKE %s
GROUP BY district
''',[gun])
rows=cursor.fetchall()
gun_crimes_per_district = pd.DataFrame(rows, columns=['dist_num','number_of_gun_crimes'])
gun_crimes_per_district['dist_num'] = gun_crimes_per_district['dist_num'].astype(str)
gun_crimes_per_district.head()
Out[36]:
In [37]:
# farthest_block_gun_crime_map = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
# farthest_block_gun_crime_map.choropleth(geo_data="Boundaries.geojson",
# fill_color='YlOrRd',
# fill_opacity=0.5,
# line_opacity=1,
# data = gun_crimes_per_district,
# key_on='feature.properties.dist_num',
# columns = ['dist_num', 'number_of_gun_crimes'],
# legend_name="GUN CRIMES PER DISTRICT"
# )
# farthest_block_gun_crime_map
In [ ]:
farthest_block_gun_crime_map = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
folium.Choropleth(geo_data="Boundaries.geojson",
fill_color='OrRd',
fill_opacity=0.5,
line_opacity=1,
data = gun_crimes_per_district,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'number_of_gun_crimes'],
legend_name="GUN CRIMES PER DISTRICT"
).add_to(farthest_block_gun_crime_map )
farthest_block_gun_crime_map
In [39]:
cursor.execute("""SELECT DISTINCT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
police_stations = cursor.fetchall()
gun='%GUN%'
for police_station in police_stations:
cursor.execute("""SELECT DISTINCT on (A.block) A.district, A.block, A.where_is,
ST_Distance(A.where_is,B.where_is)
FROM crimes as A, police_stations as B
WHERE
A.district=%s and DESCRIPTION::text LIKE %s and B.district= %s
and ST_Distance(A.where_is,B.where_is)
IN
( SELECT max(dist)
FROM
(SELECT ST_Distance(A.where_is,B.where_is) as dist
FROM crimes as A, police_stations as B
WHERE A.district=%s and DESCRIPTION::text LIKE %s and B.district= %s ) as f)
""",[police_station[2],gun,police_station[2],police_station[2],gun, police_station[2]])
farthest_block_gun_crime = cursor.fetchall()
print(farthest_block_gun_crime)
print((farthest_block_gun_crime[0][2],farthest_block_gun_crime[0][2]))
if not farthest_block_gun_crime:
continue
cursor.execute('''
SELECT ST_X(ST_AsText(%s)), ST_Y(ST_AsText(%s))
''',(farthest_block_gun_crime[0][2],farthest_block_gun_crime[0][2]))
farthest_block_gun_crime_location = cursor.fetchall()
folium.Marker(location=(police_station[0],police_station[1]),popup=folium.Popup(html="Police Station <br> District No.:%s <br> Farthest Gun_Crime Block:%s"%(farthest_block_gun_crime[0][0],farthest_block_gun_crime[0][1]))).add_to(farthest_block_gun_crime_map)
folium.CircleMarker(farthest_block_gun_crime_location[0],radius=5,color='#ff3187',popup=folium.Popup(html="District No.:%s <br> Block:%s"%(farthest_block_gun_crime[0][0],farthest_block_gun_crime[0][1]))).add_to(farthest_block_gun_crime_map)
In [40]:
print(farthest_block_gun_crime)
print((farthest_block_gun_crime[0][2],farthest_block_gun_crime[0][2]))
In [41]:
db_connection.commit()
In [ ]:
farthest_block_gun_crime_map
Requirements¶
The HTML document your are submitting along with this notebook file must have the source code and the output for the following requirements
Requirement #1:¶
- Locate the Block(s) that has the highest number of gun crimes. Create a Choropleth map where the districts are shaded in proportion to the number of gun crimes in that district. In addition, for each district find the block(s) that has the highest number of gun crimes in that district. Note that there might be a tie for the highest number of gun crimes. You need to find all such blocks. Add a popup marker (located at that district's police headquarter) that displays a DataFrame containing all such block along with the number of gun crimes for that block (i.e. the highest number of crimes for a district).
In [112]:
blocks_gun_crime_map2 = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
blocks_gun_crime_map2.choropleth(geo_data="Boundaries.geojson",
fill_color='YlOrRd',
fill_opacity=0.5,
line_opacity=1,
data = gun_crimes_per_district,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'number_of_gun_crimes'],
legend_name="GUN CRIME"
)
In [111]:
#cursor.execute("""SELECT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
# find highest gun crime block in each district
gun='%GUN%'
blocks_gun_violent_crimes2 = cursor.fetchall()
blocks_gun_violent_crimes_df2 = pd.DataFrame(blocks_gun_violent_crimes2, columns=['block','district','gun_crimes'])
df3 = pd.DataFrame(blocks_gun_violent_crimes2, columns=['block','district','gun_crimes'])
df4 = pd.DataFrame(blocks_gun_violent_crimes2, columns=['block','district','gun_crimes'])
for district in [x for x in range(1,26) if x not in [13,21,23]]:
cursor.execute("SELECT block, district, count(block) from crimes where DESCRIPTION::text LIKE %s GROUP BY block, district"
,[gun])
df2 = blocks_gun_violent_crimes_df2.query(f'district=={district}')
block_gun_violent_crimes_df3 = df2[df2.gun_crimes == df2.gun_crimes.max()]
header = block_gun_violent_crimes_df3.to_html(classes='table table-striped table-hover table-condensed table-responsive')
folium.Marker(location=police_station_location,popup=folium.Popup(html="District No: %s Block with Highest Gun Crimes: %s" %(police_station[2],header))).add_to(blocks_gun_crime_map2)
print(block_gun_violent_crimes_df3)
df4 = df3.append(block_gun_violent_crimes_df3)
In [113]:
df3.head()
Out[113]:
In [114]:
df4 = pd.DataFrame(blocks_gun_violent_crimes2, columns=['block','district','gun_crimes'])
df5 = df3[(df3['district'] == 25) & (df3['gun_crimes'] == 26)]
df5
header = df5.to_html(classes='table table-striped table-hover table-condensed table-responsive')
folium.Marker(location=police_station_location,popup=folium.Popup(html="District No: %s Block with Highest Gun Crimes: %s" %(police_station[2],header))).add_to(blocks_gun_crime_map2)
blocks_gun_crime_map2
Out[114]:
Requirement #2:¶
- Calculate the gun crimes density in every district (See Query 4.)
In [45]:
gun='%GUN%'
cursor.execute('''
SELECT district, count(district)
FROM crimes
WHERE DESCRIPTION::text LIKE %s
GROUP BY district
''',[gun])
rows = cursor.fetchall()
gun_crimes_per_district = pd.DataFrame(rows, columns=['dist_num','number_of_gun_crimes'])
gun_crimes_per_district['dist_num'] = gun_crimes_per_district['dist_num'].astype(str)
district=[]
tarea=[]
with open('Boundaries.geojson') as f:
data = json.load(f)
a = data['features'] # a is a list of district data (dictionaries)
for i in range(len(a)):# a[i] is the dictionary for ith district in Boundaries.geojson
obj=a[i]['geometry'] # list of coordinates for ith district
n= a[i]['properties'] # district number and district label for ith district
district.append(n['dist_num']) # add district number to the district list
# 1 square meter = 1/10000 hectares. area(obj) is in hectares
tarea.append(area(obj)/10000) # add the the area (in hectares) to area list
af=pd.DataFrame({'dist_num': district,'district_area_in_Hectares':tarea})
af['dist_num'] = af['dist_num'].astype(str)
final_data2= pd.merge(af, gun_crimes_per_district, on='dist_num', how='inner')
final_data2['crime_density'] = round(final_data2['number_of_gun_crimes']/(final_data2['district_area_in_Hectares']/100))
final_data2
Out[45]:
Requirement #3:¶
- Locate the farthest UNLAWFUL POSS OF HANDGUN crime from the police station in every district. Create a Choropleth map where the districts are shaded in proportion to the number of UNLAWFUL POSS OF HANDGUN crimes in that district. For each district, find the UNLAWFUL POSS OF HANDGUN crime that was farthest from police station. Add a popup on the Choropleth map to display the district number and the Block where the farthest UNLAWFUL POSS OF HANDGUN crime occured. Also add circle marker (of radius 5) at the location of the farthest UNLAWFUL POSS OF HANDGUN crime. (SEE Query 6.)
In [46]:
handguns='UNLAWFUL POSS OF HANDGUN'
cursor.execute("SELECT district, count(district) from crimes where DESCRIPTION::text LIKE %s GROUP BY district",[handguns])
unlawful_poss_handguns = cursor.fetchall()
unlawful_poss_handguns_df = pd.DataFrame(unlawful_poss_handguns, columns=['dist_num','unlawful_poss_handguns'])
unlawful_poss_handguns_df['dist_num'] = unlawful_poss_handguns_df['dist_num'].astype(str)
unlawful_poss_handguns_df
Out[46]:
In [47]:
farthest_block_unlawful_poss_handguns_map = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
folium.Choropleth(geo_data="Boundaries.geojson",
fill_color='YlOrRd',
fill_opacity=0.5,
line_opacity=1,
data = unlawful_poss_handguns_df,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'unlawful_poss_handguns'],
legend_name="UNLAWFUL POSS OF HANDGUN PER DISTRICT"
).add_to(farthest_block_unlawful_poss_handguns_map )
Out[47]:
In [48]:
cursor.execute("""SELECT DISTINCT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
police_stations = cursor.fetchall()
gun='%UNLAWFUL POSS OF HANDGUN%'
for police_station in police_stations:
cursor.execute("""SELECT DISTINCT on (A.block) A.district, A.block, A.where_is,
ST_Distance(A.where_is,B.where_is)
FROM crimes as A, police_stations as B
WHERE
A.district=%s and DESCRIPTION::text LIKE %s and B.district= %s
and ST_Distance(A.where_is,B.where_is)
IN
( SELECT max(dist)
FROM
(SELECT ST_Distance(A.where_is,B.where_is) as dist
FROM crimes as A, police_stations as B
WHERE A.district=%s and DESCRIPTION::text LIKE %s and B.district= %s ) as f)
""",[police_station[2],gun,police_station[2],police_station[2],gun, police_station[2]])
farthest_block_unlawful_poss_handguns = cursor.fetchall()
print(farthest_block_unlawful_poss_handguns)
if not farthest_block_unlawful_poss_handguns:
continue
cursor.execute('''
SELECT ST_X(ST_AsText(%s)), ST_Y(ST_AsText(%s))
''',(farthest_block_unlawful_poss_handguns[0][2],farthest_block_unlawful_poss_handguns[0][2]))
farthest_block_unlawful_poss_handguns_location = cursor.fetchall()
folium.Marker(location=(police_station[0],police_station[1]),popup=folium.Popup(html="Police Station <br> District No.:%s <br> Farthest Unlawful Handgun Possion Block:%s"%(farthest_block_unlawful_poss_handguns[0][0],farthest_block_unlawful_poss_handguns[0][1]))).add_to(farthest_block_unlawful_poss_handguns_map)
folium.CircleMarker(farthest_block_unlawful_poss_handguns_location[0],radius=5,color='#ff3187',popup=folium.Popup(html="District No.:%s <br> Block:%s"%(farthest_block_unlawful_poss_handguns[0][0],farthest_block_unlawful_poss_handguns[0][1]))).add_to(farthest_block_unlawful_poss_handguns_map)
In [49]:
farthest_block_unlawful_poss_handguns_map
Out[49]:
Requirement #4:¶
- Create Marker Clusters on Choropleth map for those gun related violent crimes that have Location Desciption as RESIDENCE in (green icon) and those that have Location Desciption as STREET in (red icon). (SEE Query 5.)
In [50]:
final_data2['dist_num'] = final_data2['dist_num'].astype(str)
final_data2.head()
Out[50]:
In [51]:
gun_crime_location_map = folium.Map(location =(41.8781, -87.6298),zoom_start=11)
folium.Choropleth(geo_data="Boundaries.geojson",
fill_color='YlOrRd',
fill_opacity=0.5,
line_opacity=1,
data = final_data2,
key_on='feature.properties.dist_num',
columns = ['dist_num', 'number_of_gun_crimes'],
legend_name="GUN CRIME"
).add_to(gun_crime_location_map )
Out[51]:
In [52]:
cursor.execute("""SELECT ST_X(ST_AsText(Where_IS)), ST_Y(ST_AsText(Where_IS)), district from police_stations where district!='Headquarters'""")
gun='%GUN%'
police_stations = cursor.fetchall()
marker_cluster = MarkerCluster().add_to(gun_crime_location_map)
for police_station in police_stations:
police_station_location = (police_station[0],police_station[1])
cursor.execute("""SELECT DISTINCT ON(caseno) caseno, block, description, count (description), location_description, latitude, longitude from crimes where district=%s and DESCRIPTION::text LIKE %s and (location_description = 'RESIDENCE' or location_description = 'STREET') GROUP BY caseno,block, DESCRIPTION, location_description, latitude, longitude""",[police_station[2],gun])
crimes_per_area = cursor.fetchall()
for crime in crimes_per_area:
if crime[4]=='RESIDENCE':
folium.Marker(location=(crime[5],crime[6]),popup=folium.Popup(html="District No: %s <br> Description: %s <br> Block: %s" %(police_station[2],crime[2],crime[1])),icon=folium.Icon(color='green', icon='ok-sign'),).add_to(marker_cluster)
else:
folium.Marker(location=(crime[5],crime[6]),popup=folium.Popup(html="District No: %s <br> Description: %s<br> Block: %s" %(police_station[2],crime[2],crime[1])),icon=folium.Icon(color='red', icon='ok-sign'),).add_to(marker_cluster)
In [52]:
gun_crime_location_map
Out[52]: